Combinatorial Testing for Deep Learning Systems

TL;DR

This paper explores the application of combinatorial testing techniques to deep learning systems to evaluate their robustness and identify vulnerabilities efficiently.

Contribution

It adapts combinatorial testing concepts for DL, proposes coverage criteria, and develops a test generation method tailored for deep learning models.

Findings

CT shows promise in testing DL robustness

Coverage criteria effectively guide test generation

Potential to detect vulnerabilities early in DL systems

Abstract

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.